Machine Learning Engineer

About Lucid At Lucid, we are creating exceptional mobility experiences through innovation to drive the world forward. Built on Lucid’s proprietary technology and software-defined vehicle architecture, our award-winning vehicles bring our “Compromise Nothing” approach to the global automotive market. That means refusing to choose between performance and sustainability, design and engineering, ambition and integrity. In Lucid Air and Lucid Gravity, we have designed and built vehicles that have redefined their segments, combining exceptional range, performance, design, and expansive space in a single experience.

We achieve this through deep vertical integration, with design, engineering, and production happening in-house across our global offices and manufacturing facilities. Our teams come from industries around the world, united by a shared commitment to excellence. By refusing to settle, you can help redefine what’s possible and shape the future of mobility.

The GeoAI team is building next-generation AI-powered data automation platforms for ADAS and autonomous driving. We are seeking a Machine Learning Engineer to develop scalable auto-labeling technologies that transform fleet-collected sensor data into high-quality training datasets with minimal human intervention. This role sits at the intersection of machine learning, computer vision, spatial AI, and large-scale data infrastructure. You will work on perception models, 3D scene reconstruction, foundation models, and automated data-generation systems that accelerate autonomous-driving development.

The Role:

Build Next-Generation Auto-Labeling Systems Design and deploy production-scale auto-labeling solutions for 2D and 3D object detection, semantic and panoptic segmentation, lane and road-structure extraction, multi-object tracking, occupancy, scene understanding, and traffic-participant behavior. Develop automated label validation, confidence scoring, consistency checks, and quality-monitoring frameworks. Build active-learning and human-in-the-loop workflows that improve label quality while reducing manual annotation effort and turnaround time. Create teacher-student and model-assisted labeling workflows for large-scale camera and LiDAR datasets.

Develop Advanced Perception and Fusion Models Research, train, and evaluate camera-based and LiDAR-based detection, segmentation, tracking, depth-estimation, and occupancy models. Develop multimodal fusion models combining camera, LiDAR, radar, GNSS/IMU, and map context. Improve temporal consistency, calibration robustness, and long-tail performance across diverse driving environments and edge cases. Build end-to-end ML workflows from raw sensor ingestion and preprocessing through annotation, quality control, and dataset delivery.

Leverage Spatial AI and 3

DGS Develop 3D Gaussian Splatting (3

DGS) and related reconstruction pipelines for large-scale driving scenes collected from fleet vehicles. Use reconstructed scenes for label propagation, synthetic annotation generation, annotation validation, rare-case augmentation, and simulation. Create scene-centric representations that improve geometric accuracy and consistency across sensors, viewpoints, and time. Apply Foundation Models to Data Automation Apply Vision-Language Models (VLMs), Vision-Language-Action (VLA) models, and multimodal foundation models to scene interpretation, label generation, verification, and edge-case discovery.

Develop natural-language and agent-based workflows that help engineers search, inspect, curate, and validate large-scale datasets. Evaluate state-of-the-art research and convert promising methods into reliable production capabilities. Productionize ML Solutions Build scalable training and inference pipelines for fleet-scale datasets and distributed GPU infrastructure. Optimize model performance, throughput, reliability, monitoring, and reproducibility across cloud and production environments. Collaborate with perception, mapping, navigation, simulation, hardware, logging, and data-platform teams to deploy end-to-end solutions.

Define and track metrics for labeling accuracy, coverage, throughput, cost, and operational efficiency.

Qualifications:

MS or PhD in Computer Science, Machine Learning, Robotics, Computer Vision, or a related field. Strong software-engineering skills in Python and/or C++, with hands-on experience using PyTorch or equivalent deep-learning frameworks. Solid understanding of deep learning, computer vision, 3D geometry,…